🔄 Why Systems of Linear Equations matter in ML They help solve for multiple unknowns at once — just like: 🔹 Predicting weights in linear regression 🔹 Solving for model parameters 🔹 Representing neurons in a network ML models = solving LOTS of equations… fast. #Day73 of

🧾 Matrix Form of Systems in ML A system like: 2x + 3y = 5 4x + y = 6 Becomes: 👉 A·X = B Where A = coefficients, X = variables, B = outputs Fast, scalable — perfect for machine learning! #Day74 of #NeuralNetworkJourney #AI #ML #MathForML #LinearAlgebra #Matrices

🧠 Solving A·X = B in ML 🔹 Matrix Inverse: X = A⁻¹·B (only if A is invertible) 🔹 Gaussian Elimination: Systematically reduces equations → solution This math powers linear regression, optimization & neural nets! #Day75 of #NeuralNetworkJourney #MathForML #AI #LinearAlgebra

🧭 What are Vector Spaces & why do they matter in ML? A vector space = a collection of vectors you can add & scale. 🔹 Features = vectors 🔹 Models = transformations in vector space 🔹 Embeddings = points in high-D spaces ML learns by moving through vector space. #Day76 of

✨ What is the Dot Product & why it matters in ML Multiply matching vector elements ➕ sum them up: A · B = |A||B|cos(θ) 🔹 Measures similarity 🔹 Powers neural nets, attention, and cosine similarity Tiny math → big insights! #Day77 of #NeuralNetworkJourney #MathForML

📏 What is Cosine Similarity in ML? It measures angle between two vectors, not their size. Formula: cos(θ) = (A · B) / (||A|| ||B||) 🔹 1 = same direction (very similar) 🔹 0 = 90° apart (unrelated) 🔹 –1 = opposite Used in NLP, search, recommendations. #Day78 of

🤖 What is an AI Agent? An AI agent doesn’t just predict — it acts. It can: 🔹 Perceive the environment 🔹 Make decisions 🔹 Take actions toward a goal Agents = LLMs + memory + tools + planning 🧠🛠️ From models → minds. #Day80 of #NeuralNetworkJourney #AI #LLMAgents

🧠 Types of AI Agents 🔹 Reactive: Responds to input, no memory (e.g. rules-based bots) 🔹 Goal-Based: Makes decisions to reach a goal 🔹 Learning Agents: Improve over time with data (like RL agents) 🔹 Autonomous: Plan, reason, act independently From rules → real

🔄 The Agent Loop: How AI Agents Think & Act 1️⃣ Observe – Sense the environment 2️⃣ Plan – Decide what to do 3️⃣ Act – Execute an action 4️⃣ Reflect – Learn from outcome Repeat = smarter behavior over time 🧠 Agents don’t just respond — they adapt. #Day82 of #NeuralNetworkJourney

🛠️ Tool-Using Agents in AI LLM agents can call: 🔹 APIs (get data) 🔹 Code interpreters (do math, run logic) 🔹 Web search (retrieve info) 🔹 Databases (fetch facts) Tools turn a smart assistant → a working assistant. #Day83 of #NeuralNetworkJourney #LLMAgents #AI #ToolUse

🧠 Planning in AI Agents Smart agents don’t just react — they plan. 🔹 Single-step: Pick & act immediately 🔹 Multi-step: Think several actions ahead 🔹 Dynamic planning: Adjust as new info arrives Planning = from chatbots → copilots 🚀 #Day84 of #NeuralNetworkJourney #AI

💾 Memory in AI Agents Without memory, agents forget everything. With memory, they can: 🔹 Remember past chats 🔹 Track task history 🔹 Personalize responses 🔹 Reflect and improve Memory = long-term intelligence, not just short-term smarts. #Day85 of #NeuralNetworkJourney #AI

🧠 Types of Memory in AI Agents 🔹 Short-Term Memory → Holds current context (e.g. recent messages) 🔹 Long-Term Memory → Stores past knowledge, facts, user history Combined = context-aware, personalized, useful agents. #Day86 of #NeuralNetworkJourney #AI #LLMAgents #Memory

📚 What is a Vector Store in AI Agents? It stores information as embeddings (vectors) so agents can: 🔹 Remember past chats 🔹 Retrieve facts & context 🔹 Search semantically Examples: FAISS, Pinecone, Weaviate Vector store = memory bank for smart agents. #Day87 of

🔎 Retrieval Strategies in AI Agents Agents don’t recall everything — they fetch what matters: 🔹 Top-k: Return top matches 🔹 MMR: Balance relevance & diversity 🔹 Filters: Add rules (e.g. date, tag) Smart memory = better context = better answers. #Day88 of

🧠 What is MCP in LLM Systems? MCP = Model + Context + Prompt 🔹 Model: The LLM (GPT, Claude, etc.) 🔹 Context: What the model sees (docs, memory, tools) 🔹 Prompt: Your question, task, or instruction Mastering MCP = mastering how LLMs think. #Day89 of #NeuralNetworkJourney

⚙️ MCP: What is the "Model"? It’s the brain of the system: 🔹 GPT, Claude, Gemini, Mistral = pre-trained LLMs 🔹 Takes in context + prompt 🔹 Outputs predictions, completions, actions But without the right context or prompt? Even genius models fail. #Day90 of

👁️ MCP: What is “Context”? Context = what the model sees before answering: 🔹 Chat history 🔹 Retrieved docs (RAG) 🔹 Tool results 🔹 Memory (vector DBs, functions, user prefs) Right context = relevant, grounded, useful replies. Garbage in → hallucination out. #Day91 of

💬 MCP: What is a Prompt? It’s your direct instruction to the model — the question, task, or command. A great prompt can: 🔹 Guide tone & structure 🔹 Clarify goals 🔹 Unlock reasoning 📌 Prompt = how you talk to AI. Better prompts → better answers. #Day92 of